McNeil, Alexander John orcid.org/0000-0002-6137-2890 and Bladt, Martin (2022) Time series with infinite-order partial copula dependence. Dependence Modeling. 87–107. ISSN 2300-2298
Abstract
Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious method for parameterizing s-vine processes using the Kendall partial autocorrelation function is developed. The potential of the resulting models to give improved statistical fits in many applications is indicated with an example using macroeconomic data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > The York Management School |
Depositing User: | Pure (York) |
Date Deposited: | 20 Apr 2022 08:20 |
Last Modified: | 01 Feb 2025 00:08 |
Published Version: | https://doi.org/10.1515/demo-2022-0105 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1515/demo-2022-0105 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185863 |
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